Kejun Huang

TL;DR: The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties; broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning.

TL;DR: Uniqueness aspects of NMF are revisited here from a geometrical point of view, and a new algorithm for symmetric NMF is proposed, which is very different from existing ones.

TL;DR: Nonnegative matrix factorization (NMF) aims to factor a data matrix into low-rank latent factor matrices with nonnegativity constraints with nonNegativity constraints.

TL;DR: In this article, a new feasible point pursuit successive convex approximation (FPP-SCA) algorithm is proposed for non-convex quadratic programs (QCQPs), which adds slack variables to sustain feasibility and a penalty to ensure slacks are sparingly used.

TL;DR: The core components are carefully designed to make the overall algorithm more scalable, including efficient methods for solving QCQP-1, memory efficient implementation, parallel/distributed implementation, and smart initialization.